Paper 2, Econ 980x

ddi <- read_ipums_ddi("atus_00005.xml")
csv <- read_ipums_micro(ddi) %>%
  clean_names()

## Use of data from IPUMS ATUS is subject to conditions including that users
## should cite the data appropriately. Use command `ipums_conditions()` for more
## details.

data

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## <bytecode: 0x156bbd238>
## <environment: namespace:utils>

#csv <- read_csv(file = "atus_00001.csv.gz") %>%
 # clean_names()

# race 
race_file <- read_excel("race_980x.xlsx")

race_file <- race_file %>%
  rename(code1 = race) %>%
  rename(race = code) 

csv <- left_join(csv, race_file, by = "race") %>%
  rename(race_code = race) %>%
  rename(race = code1)


# education
education_file <- read_excel("Education.xlsx")
education_file <- education_file %>%
  rename(code_educ = educ) %>%
  rename(educ = code_education) %>%
  mutate(educ = as.numeric(educ))

csv <- left_join(csv, education_file, by = "educ") %>%
  rename(code_education = educ) %>%
  rename(educ = code_educ) 

# marital status
marital_file <- read_excel("marital_status.xlsx")
education_file <- marital_file %>%
  mutate(marst = as.numeric(marst))
  
csv <- left_join(csv, marital_file, by = "marst") %>%
  rename(code_marst = marst) %>%
  rename(marst = marst_code) %>%
  mutate(marst_simple = ifelse(code_marst <= 2, "Married", ifelse(code_marst >= 6, "Never Married", "Separated/Divorced")))



# age
csv <- csv %>%
  filter(age > 18)

# children
csv <- csv %>%
  mutate(child = ifelse(yngch < 19, 1, 0) )

# gender

csv <- csv %>%
  mutate(gender = ifelse(sex == 1, "male", "female")) 

# years 

csv %>%
  summary(earnweek)

##       year          caseid              pernum      lineno       wt06          
##  Min.   :2003   Min.   :2.003e+13   Min.   :1   Min.   :1   Min.   :   419472  
##  1st Qu.:2006   1st Qu.:2.006e+13   1st Qu.:1   1st Qu.:1   1st Qu.:  2932235  
##  Median :2011   Median :2.011e+13   Median :1   Median :1   Median :  5230098  
##  Mean   :2011   Mean   :2.011e+13   Mean   :1   Mean   :1   Mean   :  7201674  
##  3rd Qu.:2016   3rd Qu.:2.016e+13   3rd Qu.:1   3rd Qu.:1   3rd Qu.:  8816158  
##  Max.   :2021   Max.   :2.021e+13   Max.   :1   Max.   :1   Max.   :209010030  
##                                                             NA's   :8469       
##       wt20                age             sex          race_code  
##  Min.   :        0   Min.   :19.00   Min.   :1.000   Min.   :100  
##  1st Qu.:  3606315   1st Qu.:36.00   1st Qu.:1.000   1st Qu.:100  
##  Median :  6475784   Median :48.00   Median :2.000   Median :100  
##  Mean   :  8746744   Mean   :49.48   Mean   :1.563   Mean   :104  
##  3rd Qu.: 11222184   3rd Qu.:62.00   3rd Qu.:2.000   3rd Qu.:100  
##  Max.   :137151708   Max.   :85.00   Max.   :2.000   Max.   :599  
##  NA's   :199330                                                   
##    code_marst    code_education    educyrs       empstat_cps8     fullpart    
##  Min.   :1.000   Min.   :10.0   Min.   :101.0   Min.   :1.00   Min.   : 1.00  
##  1st Qu.:1.000   1st Qu.:21.0   1st Qu.:112.0   1st Qu.:1.00   1st Qu.: 1.00  
##  Median :1.000   Median :30.0   Median :214.0   Median :1.00   Median : 1.00  
##  Mean   :2.764   Mean   :29.7   Mean   :190.4   Mean   :2.72   Mean   :37.06  
##  3rd Qu.:4.000   3rd Qu.:40.0   3rd Qu.:216.0   3rd Qu.:5.00   3rd Qu.:99.00  
##  Max.   :6.000   Max.   :43.0   Max.   :321.0   Max.   :7.00   Max.   :99.00  
##                                                                               
##     earnweek            eldch          yngch           nchild      
##  Min.   :     0.0   Min.   : 0.0   Min.   : 0.00   Min.   :0.0000  
##  1st Qu.:   673.1   1st Qu.:13.0   1st Qu.: 9.00   1st Qu.:0.0000  
##  Median :  1846.2   Median :99.0   Median :99.00   Median :0.0000  
##  Mean   : 44392.0   Mean   :60.8   Mean   :59.44   Mean   :0.8366  
##  3rd Qu.:100000.0   3rd Qu.:99.0   3rd Qu.:99.00   3rd Qu.:2.0000  
##  Max.   :100000.0   Max.   :99.0   Max.   :99.00   Max.   :9.0000  
##                                                                    
##    bls_carehh         bls_comm          bls_educ           bls_food      
##  Min.   :   0.00   Min.   :   0.00   Min.   :   0.000   Min.   :   0.00  
##  1st Qu.:   0.00   1st Qu.:   0.00   1st Qu.:   0.000   1st Qu.:  30.00  
##  Median :   0.00   Median :   0.00   Median :   0.000   Median :  60.00  
##  Mean   :  38.93   Mean   :  10.74   Mean   :   8.373   Mean   :  75.24  
##  3rd Qu.:  30.00   3rd Qu.:   0.00   3rd Qu.:   0.000   3rd Qu.: 100.00  
##  Max.   :1230.00   Max.   :1200.00   Max.   :1190.000   Max.   :1320.00  
##                                                                          
##    bls_hhact         bls_leis      bls_leis_arts      bls_leis_attend  
##  Min.   :   0.0   Min.   :   0.0   Min.   :   0.000   Min.   :  0.000  
##  1st Qu.:  15.0   1st Qu.: 155.0   1st Qu.:   0.000   1st Qu.:  0.000  
##  Median :  75.0   Median : 294.0   Median :   0.000   Median :  0.000  
##  Mean   : 124.9   Mean   : 329.4   Mean   :   5.879   Mean   :  6.128  
##  3rd Qu.: 185.0   3rd Qu.: 475.0   3rd Qu.:   0.000   3rd Qu.:  0.000  
##  Max.   :1405.0   Max.   :1439.0   Max.   :1015.000   Max.   :860.000  
##                                                                        
##  bls_leis_attsport  bls_leis_partsport bls_leis_relax    bls_leis_soc   
##  Min.   :   0.000   Min.   :   0.00    Min.   :   0.0   Min.   :   0.0  
##  1st Qu.:   0.000   1st Qu.:   0.00    1st Qu.:  90.0   1st Qu.: 130.0  
##  Median :   0.000   Median :   0.00    Median : 195.0   Median : 255.0  
##  Mean   :   1.832   Mean   :  16.25    Mean   : 245.3   Mean   : 297.7  
##  3rd Qu.:   0.000   3rd Qu.:   0.00    3rd Qu.: 356.0   3rd Qu.: 425.0  
##  Max.   :1030.000   Max.   :1125.00    Max.   :1439.0   Max.   :1439.0  
##                                                                         
##  bls_leis_soccom   bls_leis_soccomex bls_leis_sport    bls_leis_travel  
##  Min.   :   0.00   Min.   :   0.00   Min.   :   0.00   Min.   :   0.00  
##  1st Qu.:   0.00   1st Qu.:   0.00   1st Qu.:   0.00   1st Qu.:   0.00  
##  Median :   0.00   Median :   0.00   Median :   0.00   Median :   0.00  
##  Mean   :  46.38   Mean   :  40.22   Mean   :  18.15   Mean   :  13.54  
##  3rd Qu.:  60.00   3rd Qu.:  45.00   3rd Qu.:   0.00   3rd Qu.:  12.00  
##  Max.   :1350.00   Max.   :1350.00   Max.   :1125.00   Max.   :1350.00  
##                                                                         
##   bls_leis_tv       bls_pcare      bls_purch         bls_social     
##  Min.   :   0.0   Min.   :   0   Min.   :   0.00   Min.   :   0.00  
##  1st Qu.:  40.0   1st Qu.: 490   1st Qu.:   0.00   1st Qu.:   0.00  
##  Median : 122.0   Median : 564   Median :   0.00   Median :   0.00  
##  Mean   : 176.9   Mean   : 574   Mean   :  49.23   Mean   :  25.12  
##  3rd Qu.: 255.0   3rd Qu.: 645   3rd Qu.:  73.00   3rd Qu.:   0.00  
##  Max.   :1433.0   Max.   :1440   Max.   :1320.00   Max.   :1325.00  
##                                                                     
##     bls_work          race               educ              marst          
##  Min.   :   0.0   Length:216908      Length:216908      Length:216908     
##  1st Qu.:   0.0   Class :character   Class :character   Class :character  
##  Median :   0.0   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 176.7                                                           
##  3rd Qu.: 438.2                                                           
##  Max.   :1430.0                                                           
##                                                                           
##  marst_simple           child          gender         
##  Length:216908      Min.   :0.000   Length:216908     
##  Class :character   1st Qu.:0.000   Class :character  
##  Mode  :character   Median :0.000   Mode  :character  
##                     Mean   :0.385                     
##                     3rd Qu.:1.000                     
##                     Max.   :1.000                     
## 

# employed
csv <- csv %>%
  mutate(empstat_cps8 = as.numeric(empstat_cps8)) %>%
  mutate(emp = ifelse(empstat_cps8<= 2, "Employed", ifelse(empstat_cps8>=5, "Not In Labour Force", "Unemployed")))

# race
csv %>%
  count(race, sort = TRUE) %>%
  filter(n > 1000)

#We can see that top 5 groups > 1000 people

csv <- csv %>%
  filter(race %in% c("White only", "Black only", "Asian only", "American Indian, Alaskan Native", "White-American Indian")) 

view(c)

#leisure hours aggregate
csv %>%
  ggplot(mapping = aes(x = bls_leis)) +
  geom_histogram(binwidth=50) +
  labs(
  title = "Distribution of daily individual leisure minutes",
  x = "Daily Minutes",
  y = "Count",
  caption = "Data from ATUS",
) 

csv %>%
  filter(year %in% c(2007, 2009)) %>%
  ggplot(mapping = aes(x = bls_leis)) +
  geom_histogram(binwidth=50) +
  facet_wrap(~year) +
  labs(
  title = "Distribution of daily individual leisure minutes",
  x = "Daily Minutes",
  y = "Count",
  caption = "Data from ATUS",
)

#leisure hours aggregate by gender
csv %>%
  filter(year < 2020) %>%
  group_by(gender) %>%
  ggplot(mapping = aes(x = bls_leis) )+
  geom_histogram(binwidth=50) +
  facet_wrap(~ gender) +
    labs(
  title = "Distribution of daily individual leisure minutes",
  x = "Daily Minutes",
  y = "Count",
  caption = "Data from ATUS",
)

csv %>%
  filter(year %in% c(2007, 2009)) %>%
  ggplot(mapping = aes(x = bls_leis)) +
  geom_histogram(binwidth=50) +
  facet_wrap(~ gender + year) +
  labs(
  title = "Distribution of daily individual leisure minutes",
  x = "Daily Minutes",
  y = "Count",
  caption = "Data from ATUS",
)

# Median
csv %>%
  filter(year < 2020) %>%
  group_by(gender, year) %>%
  summarize(median_leisure = weighted.median(bls_leis, as.numeric(wt06), na.rm=TRUE)) %>%
  clean_names() %>%
  ggplot(aes(x = year, y = median_leisure)) +
  geom_line() +
  facet_wrap(~ gender) +
  labs(
  title = "Median daily individual leisure minutes by gender",
  y = "Daily Minutes",
  x = "Year",
  caption = "Data from ATUS",
) 

## `summarise()` has grouped output by 'gender'. You can override using the
## `.groups` argument.

# mean
csv %>%
  filter(year < 2020) %>%
  group_by(gender,year) %>%
  summarize(new = weighted.mean(bls_leis, wt06)) %>%
  clean_names() %>%
  ggplot(aes(x = year, y = new)) +
  geom_line() +
  facet_wrap(~ gender) +
  labs(
  title = "Weighted mean daily individual leisure minutes by gender",
  y = "Daily Minutes",
  x = "Year",
  caption = "Data from ATUS",
) 

## `summarise()` has grouped output by 'gender'. You can override using the
## `.groups` argument.

# Median 
csv %>%
  filter(year > 2005) %>%
  filter(year < 2011) %>%
  group_by(year, gender) %>%
  summarize(median_bls_leis = weighted.median(bls_leis, wt06)) %>%
  clean_names() %>%
  ggplot(aes(x = year, y = median_bls_leis)) +
  geom_line() +
  facet_wrap(~ gender) +
  labs(
  title = "Weighted median daily individual leisure minutes by gender",
  y = "Daily Minutes",
  x = "Year",
  caption = "Data from ATUS",
) 

## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

# Mean
csv %>%
  filter(year > 2005) %>%
  filter(year < 2011) %>%
  group_by(year, gender) %>%
  summarize(mean_bls_leis = weighted.mean(bls_leis, wt06)) %>%
  clean_names() %>%
  ggplot(aes(x = year, y = mean_bls_leis)) +
  geom_line() +
  facet_wrap(~ gender) +
  labs(
  title = "Mean daily individual leisure minutes by gender",
  y = "Daily Minutes",
  x = "Year",
  caption = "Data from ATUS",
) 

## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

# All Quantile
csv %>%
  filter(year < 2020) %>%
  group_by(year, gender) %>%
  summarize(quantile_25 = weighted.quantile(bls_leis, wt06, prob = 0.25), quantile_50 = weighted.quantile(bls_leis, wt06, prob = 0.50), quantile_75 = weighted.quantile(bls_leis, wt06, prob = 0.75)) %>%
  clean_names() %>%
  pivot_longer(quantile_25:quantile_75, names_to = "quantile", values_to = "hours") %>%
  ggplot(aes(x = year, y = hours, group = quantile, colour = quantile)) +
  geom_line() +
    facet_wrap(~ gender) +
  labs(
  title = "Weighted daily individual leisure minutes by gender",
  y = "Daily Minutes",
  x = "Year",
  caption = "Data from ATUS",
) 

## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

# by year, gender
csv %>%
  filter(year < 2020) %>%
  group_by(year, gender) %>%
  summarize(quantile_25 = weighted.quantile(bls_leis, wt06, prob = 0.25), quantile_50 = weighted.quantile(bls_leis, wt06, prob = 0.50), quantile_75 = weighted.quantile(bls_leis, wt06, prob = 0.75)) %>%
  clean_names() %>%
  pivot_longer(quantile_25:quantile_75, names_to = "quantile", values_to = "hours") %>%
  filter(year > 2005) %>%
  filter(year < 2011) %>%
  ggplot(aes(x = year, y = hours, group = quantile, colour = quantile)) +
  geom_line() +
  facet_wrap(~gender) +
    labs(
  title = "Weighted daily individual leisure minutes by gender",
  y = "Daily Minutes",
  x = "Year",
  caption = "Data from ATUS",
) 

## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

# by year, gender
csv %>%
  group_by(year, gender) %>%
  summarize(median = median(bls_leis)) %>%
  clean_names() %>%
  filter(year > 2005) %>%
  filter(year < 2011) %>%
  ggplot(aes(x = year, y = median)) +
  geom_line() +
  facet_wrap(~gender) +
      labs(
  title = "Daily individual leisure minutes by gender",
  y = "Daily Minutes",
  x = "Year",
  caption = "Data from ATUS",
) 

## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

#csv %>%
 # ggplot(mapping = aes(x = year, y = bls_leis)) + 
 # geom_jitter(alpha = 0.01)

#naive regression of men versus women
  

g <- csv %>%
  lm(formula = bls_leis ~ gender, weight = wt06)
summary(g)

## 
## Call:
## lm(formula = bls_leis ~ gender, data = ., weights = wt06)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -3873300  -353387   -33870   343917  6366061 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 291.1692     0.6646  438.10   <2e-16 ***
## gendermale   39.5660     0.9574   41.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 583600 on 206819 degrees of freedom
##   (8382 observations deleted due to missingness)
## Multiple R-squared:  0.008191,   Adjusted R-squared:  0.008186 
## F-statistic:  1708 on 1 and 206819 DF,  p-value: < 2.2e-16

#naive regression of men versus women + year 

y <- csv %>%
  lm(formula = bls_leis ~ gender + year, weight = wt06)

# race count
newcsv <- csv %>%
 filter(race_code <= 210)

newcsv

newcsv %>%
  lm(formula = bls_leis ~ gender + year + age + race, weight = wt06) 

## 
## Call:
## lm(formula = bls_leis ~ gender + year + age + race, data = ., 
##     weights = wt06)
## 
## Coefficients:
##               (Intercept)                 gendermale  
##                  16.98725                   44.88100  
##                      year                        age  
##                   0.07276                    3.15759  
##            raceAsian only             raceBlack only  
##                 -65.93934                   15.86296  
##            raceWhite only  raceWhite-American Indian  
##                 -28.02202                  -10.83630

# 
a <- newcsv %>%
  lm(formula = bls_leis ~ gender + year + age + race, weight = wt06) 
summary(a)

## 
## Call:
## lm(formula = bls_leis ~ gender + year + age + race, data = ., 
##     weights = wt06)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -3099397  -342415   -41506   308384  7238310 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                16.98725  176.24323   0.096  0.92321    
## gendermale                 44.88100    0.92435  48.554  < 2e-16 ***
## year                        0.07276    0.08759   0.831  0.40618    
## age                         3.15759    0.02661 118.658  < 2e-16 ***
## raceAsian only            -65.93934    5.79279 -11.383  < 2e-16 ***
## raceBlack only             15.86296    5.47901   2.895  0.00379 ** 
## raceWhite only            -28.02202    5.34053  -5.247 1.55e-07 ***
## raceWhite-American Indian -10.83630    7.81604  -1.386  0.16562    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 562700 on 206813 degrees of freedom
##   (8382 observations deleted due to missingness)
## Multiple R-squared:  0.07786,    Adjusted R-squared:  0.07783 
## F-statistic:  2494 on 7 and 206813 DF,  p-value: < 2.2e-16

newcsv

# education
newcsv1 <- newcsv %>%
  mutate(new_educ = ifelse(code_education <= 17, "<High School", ifelse(code_education >= 40, ">Four Year Degree", "High School Graduate")))

  
b <- newcsv1 %>%
  lm(formula = bls_leis ~ gender + year + age + race + new_educ, weight = wt06) 
summary(b)

## 
## Call:
## lm(formula = bls_leis ~ gender + year + age + race + new_educ, 
##     data = ., weights = wt06)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -3205427  -332218   -33792   311237  7647075 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -767.53425  175.41369  -4.376 1.21e-05 ***
## gendermale                     44.48196    0.91728  48.493  < 2e-16 ***
## year                            0.47850    0.08721   5.487 4.10e-08 ***
## age                             3.10516    0.02645 117.377  < 2e-16 ***
## raceAsian only                -39.06627    5.76687  -6.774 1.25e-11 ***
## raceBlack only                 21.64222    5.43741   3.980 6.89e-05 ***
## raceWhite only                -16.10388    5.30318  -3.037  0.00239 ** 
## raceWhite-American Indian      -5.47262    7.75613  -0.706  0.48045    
## new_educ>Four Year Degree     -77.85439    1.61267 -48.277  < 2e-16 ***
## new_educHigh School Graduate  -28.36747    1.50248 -18.880  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 558300 on 206811 degrees of freedom
##   (8382 observations deleted due to missingness)
## Multiple R-squared:  0.09221,    Adjusted R-squared:  0.09217 
## F-statistic:  2334 on 9 and 206811 DF,  p-value: < 2.2e-16

# with children
 

c <- newcsv1 %>%
  lm(formula = bls_leis ~ gender + year + age + race + new_educ + nchild, weight = wt06) 
summary(c)

## 
## Call:
## lm(formula = bls_leis ~ gender + year + age + race + new_educ + 
##     nchild, data = ., weights = wt06)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -3411543  -317557   -22904   312665  7269668 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -459.06351  173.17810  -2.651  0.00803 ** 
## gendermale                     41.22445    0.90640  45.482  < 2e-16 ***
## year                            0.35450    0.08609   4.118 3.83e-05 ***
## age                             2.68428    0.02672 100.460  < 2e-16 ***
## raceAsian only                -44.53755    5.69220  -7.824 5.13e-15 ***
## raceBlack only                 15.60231    5.36718   2.907  0.00365 ** 
## raceWhite only                -22.53617    5.23480  -4.305 1.67e-05 ***
## raceWhite-American Indian     -15.36305    7.65623  -2.007  0.04479 *  
## new_educ>Four Year Degree     -84.51939    1.59420 -53.017  < 2e-16 ***
## new_educHigh School Graduate  -37.72979    1.48827 -25.352  < 2e-16 ***
## nchild                        -30.89065    0.41662 -74.145  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 551100 on 206810 degrees of freedom
##   (8382 observations deleted due to missingness)
## Multiple R-squared:  0.1157, Adjusted R-squared:  0.1157 
## F-statistic:  2706 on 10 and 206810 DF,  p-value: < 2.2e-16

#marital status

d <- newcsv1 %>%
  lm(formula = bls_leis ~ gender + year + age + race + new_educ + nchild + marst_simple, weight = wt06) 
summary(d)

## 
## Call:
## lm(formula = bls_leis ~ gender + year + age + race + new_educ + 
##     nchild + marst_simple, data = ., weights = wt06)
## 
## Weighted Residuals:
##      Min       1Q   Median       3Q      Max 
## -3573012  -318323   -24660   309070  7016453 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    -13.59818  172.67882  -0.079 0.937233    
## gendermale                      42.06056    0.90823  46.310  < 2e-16 ***
## year                             0.10110    0.08589   1.177 0.239151    
## age                              3.33576    0.03273 101.929  < 2e-16 ***
## raceAsian only                 -38.52564    5.66762  -6.797 1.07e-11 ***
## raceBlack only                  10.40823    5.34310   1.948 0.051419 .  
## raceWhite only                 -17.97103    5.21122  -3.449 0.000564 ***
## raceWhite-American Indian      -11.93607    7.62035  -1.566 0.117270    
## new_educ>Four Year Degree      -77.05326    1.59799 -48.219  < 2e-16 ***
## new_educHigh School Graduate   -34.25177    1.48356 -23.088  < 2e-16 ***
## nchild                         -22.37572    0.45781 -48.876  < 2e-16 ***
## marst_simpleNever Married       58.01406    1.34945  42.991  < 2e-16 ***
## marst_simpleSeparated/Divorced  24.53589    1.26743  19.359  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 548500 on 206808 degrees of freedom
##   (8382 observations deleted due to missingness)
## Multiple R-squared:  0.1241, Adjusted R-squared:  0.124 
## F-statistic:  2441 on 12 and 206808 DF,  p-value: < 2.2e-16

stargazer(a, b, c, d, title="Various Models", align=TRUE, type = "html")

Various Models

	Dependent variable:
	bls_leis
	(1)	(2)	(3)	(4)
gendermale	44.881***	44.482***	41.224***	42.061***
	(0.924)	(0.917)	(0.906)	(0.908)
year	0.073	0.479***	0.354***	0.101
	(0.088)	(0.087)	(0.086)	(0.086)
age	3.158***	3.105***	2.684***	3.336***
	(0.027)	(0.026)	(0.027)	(0.033)
raceAsian only	-65.939***	-39.066***	-44.538***	-38.526***
	(5.793)	(5.767)	(5.692)	(5.668)
raceBlack only	15.863***	21.642***	15.602***	10.408*
	(5.479)	(5.437)	(5.367)	(5.343)
raceWhite only	-28.022***	-16.104***	-22.536***	-17.971***
	(5.341)	(5.303)	(5.235)	(5.211)
raceWhite-American Indian	-10.836	-5.473	-15.363**	-11.936
	(7.816)	(7.756)	(7.656)	(7.620)
new_educ> Four Year Degree		-77.854***	-84.519***	-77.053***
		(1.613)	(1.594)	(1.598)
new_educHigh School Graduate		-28.367***	-37.730***	-34.252***
		(1.502)	(1.488)	(1.484)
nchild			-30.891***	-22.376***
			(0.417)	(0.458)
marst_simpleNever Married				58.014***
				(1.349)
marst_simpleSeparated/Divorced				24.536***
				(1.267)
Constant	16.987	-767.534***	-459.064***	-13.598
	(176.243)	(175.414)	(173.178)	(172.679)
Observations	206,821	206,821	206,821	206,821
R2	0.078	0.092	0.116	0.124
Adjusted R2	0.078	0.092	0.116	0.124
Residual Std. Error	562,740.400 (df = 206813)	558,347.100 (df = 206811)	551,072.100 (df = 206810)	548,461.500 (df = 206808)
F Statistic	2,494.496*** (df = 7; 206813)	2,334.080*** (df = 9; 206811)	2,706.253*** (df = 10; 206810)	2,441.352*** (df = 12; 206808)
Note:	p<0.1; p<0.05; p<0.01

newcsv1 %>%
  filter(year > 2006) %>%
  filter(year < 2011) %>%
  group_by(year) %>%
  count(emp) %>%
  filter(emp == "Unemployed") %>%
  ggplot(mapping = aes(x = year, y = n)) +
  geom_line() +
  labs(
  title = "Unemployment",
  y = "Daily Minutes",
  x = "Count",
  caption = "Data from ATUS",
)

  #facet_wrap(~emp)

newcsv1 %>%
  ggplot(mapping = aes(x = bls_leis)) +
  geom_histogram() +
  facet_wrap(~emp) +
  labs(
  title = "Daily individual leisure minutes by employment type",
  y = "Count",
  x = "Daily Minutes",
  caption = "Data from ATUS",
)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

newcsv1 %>%
  filter(year < 2020) %>%
  group_by(emp, gender, new_educ, year) %>%
  summarize(median = weighted.median(bls_leis, wt06)) %>%
  ggplot(mapping = aes(x = year, y = median, group = new_educ, colour = new_educ)) +
  geom_line() +
  facet_wrap(~gender + emp) +
  labs(
  title = "Weighted daily individual leisure minutes",
  y = "Daily Minutes",
  x = "Count",
  caption = "Data from ATUS",
)

## `summarise()` has grouped output by 'emp', 'gender', 'new_educ'. You can
## override using the `.groups` argument.

newcsv1 %>%
  filter(year < 2020) %>%
  group_by(emp, gender, new_educ, year) %>%
  summarize(median = median(bls_leis)) %>%
  ggplot(mapping = aes(x = year, y = median, group = new_educ, colour = new_educ)) +
  geom_line() +
  facet_wrap(~gender + emp) +
  labs(
  title = "Daily individual leisure minutes",
  y = "Daily Minutes",
  x = "Count",
  caption = "Data from ATUS",
)

## `summarise()` has grouped output by 'emp', 'gender', 'new_educ'. You can
## override using the `.groups` argument.

newcsv1 %>%
  filter(year > 2006) %>%
  filter(year < 2011) %>%
  group_by(emp, gender, new_educ, year) %>%
  summarize(median = weighted.median(bls_leis, wt06)) %>%
  ggplot(mapping = aes(x = year, y = median, group = new_educ, colour = new_educ)) +
  geom_line() +
  facet_wrap(~gender + emp) +
  labs(
  title = "Weighted daily individual leisure minutes",
  y = "Daily Minutes",
  x = "Count",
  caption = "Data from ATUS",
)

## `summarise()` has grouped output by 'emp', 'gender', 'new_educ'. You can
## override using the `.groups` argument.

newcsv1 %>%
  filter(year > 2006) %>%
  filter(year < 2011) %>%
  group_by(emp, gender, new_educ, year) %>%
  summarize(median = median(bls_leis)) %>%
  ggplot(mapping = aes(x = year, y = median, group = new_educ, colour = new_educ)) +
  geom_line() +
  facet_wrap(~gender + emp) +
  labs(
  title = "Daily individual leisure minutes",
  y = "Daily Minutes",
  x = "Count",
  caption = "Data from ATUS",
)

## `summarise()` has grouped output by 'emp', 'gender', 'new_educ'. You can
## override using the `.groups` argument.

count_table <- newcsv1 %>%
  filter(year > 2006) %>%
  filter(year < 2011) %>%
  group_by(emp, gender) %>%
  tally()

count_table2 <- newcsv1 %>%
  filter(year > 2006) %>%
  filter(year < 2011) %>%
  group_by(emp, gender, new_educ) %>%
  tally()
  
kable(count_table2) %>% kable_styling()

emp	gender	new_educ	n
Employed	female	<High School	897
Employed	female	>Four Year Degree	6163
Employed	female	High School Graduate	8703
Employed	male	<High School	1260
Employed	male	>Four Year Degree	5823
Employed	male	High School Graduate	7913
Not In Labour Force	female	<High School	2104
Not In Labour Force	female	>Four Year Degree	2236
Not In Labour Force	female	High School Graduate	6251
Not In Labour Force	male	<High School	996
Not In Labour Force	male	>Four Year Degree	1059
Not In Labour Force	male	High School Graduate	2800
Unemployed	female	<High School	130
Unemployed	female	>Four Year Degree	216
Unemployed	female	High School Graduate	613
Unemployed	male	<High School	144
Unemployed	male	>Four Year Degree	168
Unemployed	male	High School Graduate	581

kable(count_table) %>% kable_styling()

emp	gender	n
Employed	female	15763
Employed	male	14996
Not In Labour Force	female	10591
Not In Labour Force	male	4855
Unemployed	female	959
Unemployed	male	893

newcsv1 %>%
  filter(year > 2006) %>%
  filter(year < 2011) %>%
  group_by(emp, year) %>%
  tally() %>%
  pivot_wider(names_from = year, values_from = n) %>%
  clean_names() %>%
  mutate(change_2008 = x2008-x2007) %>%
  mutate(change_2009 = x2009-x2008) %>%
  mutate(change_2010 = x2010-x2009) %>%
  select(1, 6:8) %>%
  kbl() %>%
  kable_styling()

emp	change_2008	change_2009	change_2010
Employed	305	-3	-70
Not In Labour Force	111	241	7
Unemployed	57	250	129

#Change

change <- newcsv1 %>%
  filter(year<2020) %>%
  group_by(gender,  year, new_educ, child, marst_simple) %>%
  summarize(median_bls_leis = weighted.median(bls_leis, wt06)) %>%
  clean_names() %>%
  pivot_wider(values_from = median_bls_leis, names_from = year) %>%
  clean_names() %>%
  mutate(leisure_change = x2009 - x2007)

## `summarise()` has grouped output by 'gender', 'year', 'new_educ', 'child'. You
## can override using the `.groups` argument.

change11 <- newcsv1 %>%
  filter(year<2020) %>%
  group_by(gender,  year, new_educ, child, marst_simple, emp) %>%
   summarize(median_bls_leis = weighted.median(bls_leis, wt06)) %>%
  clean_names() %>%
  pivot_wider(values_from = median_bls_leis, names_from = year) %>%
  clean_names() %>%
  mutate(leisure_change = x2009 - x2007)

## `summarise()` has grouped output by 'gender', 'year', 'new_educ', 'child',
## 'marst_simple'. You can override using the `.groups` argument.

change2 <- newcsv1 %>%
  filter(year<2020) %>%
  group_by(gender, year) %>%
  summarize(median_bls_leis = weighted.median(bls_leis, wt06)) %>%
  clean_names() %>%
  pivot_wider(values_from = median_bls_leis, names_from = year) %>%
  clean_names() %>%
  mutate(leisure_change = x2009 - x2007)

## `summarise()` has grouped output by 'gender'. You can override using the
## `.groups` argument.

change3 <- newcsv1 %>%
  filter(year<2020) %>%
  group_by(gender,  year, new_educ, nchild, marst_simple) %>%
   summarize(median_bls_leis = weighted.median(bls_leis, wt06)) %>%
  clean_names() %>%
  pivot_wider(values_from = median_bls_leis, names_from = year) %>%
  clean_names() %>%
  mutate(leisure_change = x2009 - x2007)

## `summarise()` has grouped output by 'gender', 'year', 'new_educ', 'nchild'. You
## can override using the `.groups` argument.

change4 <- newcsv1 %>%
  filter(year<2020) %>%
  group_by(gender,  year, new_educ, nchild, marst_simple) %>%
  summarize(mean(bls_leis)) %>%
  clean_names() %>%
  pivot_wider(values_from = mean_bls_leis, names_from = year) %>%
  clean_names() %>%
  mutate(leisure_change = x2009 - x2007)

## `summarise()` has grouped output by 'gender', 'year', 'new_educ', 'nchild'. You
## can override using the `.groups` argument.

change12 <- newcsv1 %>%
  filter(year<2020) %>%
  group_by(gender,  year, emp) %>%
   summarize(median_bls_leis = weighted.median(bls_leis, wt06)) %>%
  clean_names() %>%
  pivot_wider(values_from = median_bls_leis, names_from = year) %>%
  clean_names() %>%
  mutate(leisure_change = x2009 - x2007)

## `summarise()` has grouped output by 'gender', 'year'. You can override using
## the `.groups` argument.

#child
newcsv1

delta <- change %>%
  lm(formula = leisure_change ~ gender+ new_educ+ child+ marst_simple)
summary(delta)

## 
## Call:
## lm(formula = leisure_change ~ gender + new_educ + child + marst_simple, 
##     data = .)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -102.31  -45.97   -4.24   20.53  327.07 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                       15.75      35.42   0.445   0.6598  
## gendermale                        26.56      26.77   0.992   0.3295  
## new_educ>Four Year Degree        -54.63      32.79  -1.666   0.1065  
## new_educHigh School Graduate     -59.46      32.79  -1.813   0.0802 .
## child                             25.33      26.77   0.946   0.3519  
## marst_simpleNever Married         29.62      32.79   0.903   0.3737  
## marst_simpleSeparated/Divorced    55.29      32.79   1.686   0.1025  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 80.32 on 29 degrees of freedom
## Multiple R-squared:  0.2325, Adjusted R-squared:  0.07367 
## F-statistic: 1.464 on 6 and 29 DF,  p-value: 0.2253

delta11 <- change11 %>%
  lm(formula = leisure_change ~ gender+ new_educ+ child+ marst_simple + emp)
summary(delta11)

## 
## Call:
## lm(formula = leisure_change ~ gender + new_educ + child + marst_simple + 
##     emp, data = .)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -425.50  -44.46   -2.07   51.23  564.03 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)
## (Intercept)                     -16.928     38.221  -0.443    0.659
## gendermale                       38.210     25.429   1.503    0.136
## new_educ>Four Year Degree       -28.840     31.364  -0.920    0.360
## new_educHigh School Graduate     -8.585     30.726  -0.279    0.781
## child                            29.654     25.429   1.166    0.246
## marst_simpleNever Married        -0.576     30.726  -0.019    0.985
## marst_simpleSeparated/Divorced   26.363     30.999   0.850    0.397
## empNot In Labour Force           20.970     30.467   0.688    0.493
## empUnemployed                    31.192     31.303   0.996    0.322
## 
## Residual standard error: 128.3 on 94 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.06103,    Adjusted R-squared:  -0.01888 
## F-statistic: 0.7637 on 8 and 94 DF,  p-value: 0.6354

delta12 <- change12 %>%
  lm(formula = leisure_change ~ gender + emp)
summary(delta12)

## 
## Call:
## lm(formula = leisure_change ~ gender + emp, data = .)
## 
## Residuals:
##       1       2       3       4       5       6 
## -4.5833  3.9167  0.6667  4.5833 -3.9167 -0.6667 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)
## (Intercept)               9.583      4.953   1.935    0.193
## gendermale              -14.167      4.953  -2.860    0.104
## empNot In Labour Force    3.000      6.066   0.495    0.670
## empUnemployed             3.750      6.066   0.618    0.599
## 
## Residual standard error: 6.066 on 2 degrees of freedom
## Multiple R-squared:  0.8115, Adjusted R-squared:  0.5288 
## F-statistic:  2.87 on 3 and 2 DF,  p-value: 0.269

delta4 <- change4 %>%
  lm(formula = leisure_change ~ gender+ new_educ+ nchild+ marst_simple)
summary(delta4)

## 
## Call:
## lm(formula = leisure_change ~ gender + new_educ + nchild + marst_simple, 
##     data = .)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -485.10  -36.83   -1.40   37.40  411.07 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                      12.665     30.131   0.420   0.6752  
## gendermale                       17.502     21.562   0.812   0.4191  
## new_educ>Four Year Degree        -9.619     27.471  -0.350   0.7270  
## new_educHigh School Graduate    -57.623     26.066  -2.211   0.0296 *
## nchild                            5.253      6.039   0.870   0.3867  
## marst_simpleNever Married        18.920     26.865   0.704   0.4831  
## marst_simpleSeparated/Divorced   20.875     26.184   0.797   0.4274  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 106 on 90 degrees of freedom
##   (56 observations deleted due to missingness)
## Multiple R-squared:  0.07284,    Adjusted R-squared:  0.01103 
## F-statistic: 1.178 on 6 and 90 DF,  p-value: 0.325

delta3 <- change4 %>%
  lm(formula = leisure_change ~ gender+ new_educ+ nchild+ marst_simple)
summary(delta3)

## 
## Call:
## lm(formula = leisure_change ~ gender + new_educ + nchild + marst_simple, 
##     data = .)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -485.10  -36.83   -1.40   37.40  411.07 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                      12.665     30.131   0.420   0.6752  
## gendermale                       17.502     21.562   0.812   0.4191  
## new_educ>Four Year Degree        -9.619     27.471  -0.350   0.7270  
## new_educHigh School Graduate    -57.623     26.066  -2.211   0.0296 *
## nchild                            5.253      6.039   0.870   0.3867  
## marst_simpleNever Married        18.920     26.865   0.704   0.4831  
## marst_simpleSeparated/Divorced   20.875     26.184   0.797   0.4274  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 106 on 90 degrees of freedom
##   (56 observations deleted due to missingness)
## Multiple R-squared:  0.07284,    Adjusted R-squared:  0.01103 
## F-statistic: 1.178 on 6 and 90 DF,  p-value: 0.325

delta <- change %>%
  lm(formula = leisure_change ~ gender+ new_educ+ child+ marst_simple)
summary(delta)

## 
## Call:
## lm(formula = leisure_change ~ gender + new_educ + child + marst_simple, 
##     data = .)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -102.31  -45.97   -4.24   20.53  327.07 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                       15.75      35.42   0.445   0.6598  
## gendermale                        26.56      26.77   0.992   0.3295  
## new_educ>Four Year Degree        -54.63      32.79  -1.666   0.1065  
## new_educHigh School Graduate     -59.46      32.79  -1.813   0.0802 .
## child                             25.33      26.77   0.946   0.3519  
## marst_simpleNever Married         29.62      32.79   0.903   0.3737  
## marst_simpleSeparated/Divorced    55.29      32.79   1.686   0.1025  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 80.32 on 29 degrees of freedom
## Multiple R-squared:  0.2325, Adjusted R-squared:  0.07367 
## F-statistic: 1.464 on 6 and 29 DF,  p-value: 0.2253

delta3 <- change3 %>%
  lm(formula = leisure_change ~ gender+ new_educ+ nchild+ marst_simple)
summary(delta3)

## 
## Call:
## lm(formula = leisure_change ~ gender + new_educ + nchild + marst_simple, 
##     data = .)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -498.92  -50.39   -5.51   42.70  317.13 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                      48.757     31.561   1.545  0.12589   
## gendermale                       12.490     22.586   0.553  0.58164   
## new_educ>Four Year Degree       -38.077     28.775  -1.323  0.18911   
## new_educHigh School Graduate    -83.422     27.303  -3.055  0.00296 **
## nchild                           -2.675      6.325  -0.423  0.67338   
## marst_simpleNever Married        33.372     28.140   1.186  0.23878   
## marst_simpleSeparated/Divorced   11.390     27.426   0.415  0.67890   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 111.1 on 90 degrees of freedom
##   (56 observations deleted due to missingness)
## Multiple R-squared:  0.1187, Adjusted R-squared:   0.06 
## F-statistic: 2.021 on 6 and 90 DF,  p-value: 0.07087

stargazer(delta, delta3, title="Models for Change in Leisure", align=TRUE, type = "html")

Models for Change in Leisure

	Dependent variable:
	leisure_change
	(1)	(2)
gendermale	26.556	12.490
	(26.774)	(22.586)
new_educ> Four Year Degree	-54.625	-38.077
	(32.791)	(28.775)
new_educHigh School Graduate	-59.458*	-83.422***
	(32.791)	(27.303)
child	25.333
	(26.774)
nchild		-2.675
		(6.325)
marst_simpleNever Married	29.625	33.372
	(32.791)	(28.140)
marst_simpleSeparated/Divorced	55.292	11.390
	(32.791)	(27.426)
Constant	15.750	48.757
	(35.418)	(31.561)
Observations	36	97
R2	0.232	0.119
Adjusted R2	0.074	0.060
Residual Std. Error	80.321 (df = 29)	111.064 (df = 90)
F Statistic	1.464 (df = 6; 29)	2.021* (df = 6; 90)
Note:	p<0.1; p<0.05; p<0.01

# First Set

time_model <- newcsv1 %>%
  filter(year < 2020) %>%
  mutate(year = as.character(year)) %>%
  lm(formula = bls_leis ~ gender + year + gender*year + age + race + new_educ,weights = wt06 ) 


# W employment
time_model1 <- newcsv1 %>%
  filter(year < 2020) %>%
  mutate(year = as.character(year)) %>%
  lm(formula = bls_leis ~ gender + year + gender*year + age + race + new_educ + emp, weights = wt06) 


# W emp*gender

time_model2 <- newcsv1 %>%
  filter(year < 2020) %>%
  mutate(year = as.character(year)) %>%
  lm(formula = bls_leis ~ gender + year + gender*year + age + race + new_educ + emp + emp*year, weights = wt06) 

stargazer(time_model, time_model1, time_model2, title="Models for Change in Leisure", align=TRUE, type = "html")

Models for Change in Leisure

	Dependent variable:
	bls_leis
	(1)	(2)	(3)
gendermale	39.829***	58.683***	56.412***
	(4.027)	(3.876)	(3.937)
year2004	2.681	1.926	-1.407
	(3.935)	(3.785)	(4.499)
year2005	-0.739	0.015	-1.114
	(3.925)	(3.774)	(4.476)
year2006	-4.414	-4.360	-6.608
	(3.918)	(3.768)	(4.477)
year2007	-2.882	-0.944	-6.943
	(3.911)	(3.761)	(4.447)
year2008	3.106	4.451	-3.258
	(3.899)	(3.750)	(4.444)
year2009	7.625*	6.642*	4.859
	(3.891)	(3.742)	(4.451)
year2010	-0.655	-0.828	-5.133
	(3.887)	(3.738)	(4.439)
year2011	5.361	3.698	-0.861
	(3.875)	(3.727)	(4.438)
year2012	9.270**	6.612*	1.999
	(3.855)	(3.707)	(4.427)
year2013	9.821**	7.287**	-0.636
	(3.849)	(3.701)	(4.415)
year2014	9.443**	6.297*	-5.211
	(3.838)	(3.691)	(4.409)
year2015	3.608	1.368	-1.531
	(3.830)	(3.684)	(4.387)
year2016	-1.301	-4.232	-6.159
	(3.821)	(3.674)	(4.388)
year2017	11.251***	8.194**	-3.354
	(3.813)	(3.667)	(4.382)
year2018	3.638	3.225	-3.053
	(3.803)	(3.657)	(4.332)
year2019	5.749	6.586*	-0.647
	(3.805)	(3.660)	(4.330)
age	3.094***	1.709***	1.709***
	(0.027)	(0.029)	(0.029)
raceAsian only	-37.695***	-36.472***	-36.648***
	(5.873)	(5.648)	(5.647)
raceBlack only	24.153***	26.537***	26.499***
	(5.522)	(5.310)	(5.310)
raceWhite only	-14.910***	-5.620	-5.796
	(5.383)	(5.178)	(5.177)
raceWhite-American Indian	-4.329	-1.609	-1.628
	(7.857)	(7.556)	(7.556)
new_educ> Four Year Degree	-79.114***	-40.393***	-40.544***
	(1.638)	(1.604)	(1.604)
new_educHigh School Graduate	-29.473***	-8.872***	-9.037***
	(1.521)	(1.472)	(1.473)
empNot In Labour Force		136.188***	122.813***
		(1.093)	(4.319)
empUnemployed		95.913***	98.418***
		(2.487)	(10.932)
gendermale:year2004	6.486	4.541	6.086
	(5.674)	(5.457)	(5.535)
gendermale:year2005	3.416	1.430	2.338
	(5.661)	(5.444)	(5.523)
gendermale:year2006	8.166	7.070	8.769
	(5.645)	(5.429)	(5.511)
gendermale:year2007	6.286	5.090	7.715
	(5.628)	(5.413)	(5.492)
gendermale:year2008	2.926	1.641	4.928
	(5.616)	(5.400)	(5.484)
gendermale:year2009	1.905	-1.026	-0.024
	(5.603)	(5.388)	(5.466)
gendermale:year2010	10.019*	4.883	6.773
	(5.595)	(5.381)	(5.456)
gendermale:year2011	0.772	-2.884	-1.599
	(5.581)	(5.367)	(5.441)
gendermale:year2012	11.527**	8.987*	11.115**
	(5.555)	(5.342)	(5.418)
gendermale:year2013	1.888	-1.915	0.820
	(5.544)	(5.332)	(5.404)
gendermale:year2014	6.157	4.827	8.595
	(5.534)	(5.322)	(5.399)
gendermale:year2015	3.626	0.517	2.544
	(5.517)	(5.306)	(5.375)
gendermale:year2016	7.718	8.137	9.565*
	(5.508)	(5.296)	(5.376)
gendermale:year2017	-4.234	-4.298	0.283
	(5.496)	(5.285)	(5.363)
gendermale:year2018	13.606**	7.703	10.325*
	(5.476)	(5.267)	(5.329)
gendermale:year2019	1.795	-2.721	0.020
	(5.477)	(5.268)	(5.335)
year2004:empNot In Labour Force			8.477
			(6.006)
year2005:empNot In Labour Force			3.656
			(6.017)
year2006:empNot In Labour Force			8.783
			(5.979)
year2007:empNot In Labour Force			15.855***
			(5.979)
year2008:empNot In Labour Force			20.036***
			(5.974)
year2009:empNot In Labour Force			4.625
			(5.954)
year2010:empNot In Labour Force			10.931*
			(5.955)
year2011:empNot In Labour Force			8.703
			(5.915)
year2012:empNot In Labour Force			12.243**
			(5.867)
year2013:empNot In Labour Force			18.586***
			(5.842)
year2014:empNot In Labour Force			26.201***
			(5.827)
year2015:empNot In Labour Force			10.730*
			(5.783)
year2016:empNot In Labour Force			7.608
			(5.788)
year2017:empNot In Labour Force			30.558***
			(5.781)
year2018:empNot In Labour Force			16.597***
			(5.726)
year2019:empNot In Labour Force			17.916***
			(5.758)
year2004:empUnemployed			0.779
			(15.381)
year2005:empUnemployed			-12.601
			(15.241)
year2006:empUnemployed			-44.486***
			(16.016)
year2007:empUnemployed			-5.890
			(17.242)
year2008:empUnemployed			-1.336
			(15.701)
year2009:empUnemployed			-2.220
			(13.948)
year2010:empUnemployed			-0.594
			(13.538)
year2011:empUnemployed			23.668*
			(13.760)
year2012:empUnemployed			-4.078
			(13.983)
year2013:empUnemployed			15.636
			(14.099)
year2014:empUnemployed			31.286**
			(14.458)
year2015:empUnemployed			-43.427***
			(15.375)
year2016:empUnemployed			-37.259**
			(16.119)
year2017:empUnemployed			-19.841
			(15.413)
year2018:empUnemployed			-9.487
			(16.974)
year2019:empUnemployed			15.429
			(16.728)
Constant	191.251***	169.998***	175.466***
	(6.251)	(6.019)	(6.251)
Observations	198,136	198,136	198,136
R2	0.093	0.161	0.161
Adjusted R2	0.092	0.161	0.161
Residual Std. Error	552,444.400 (df = 198095)	531,266.400 (df = 198093)	531,134.900 (df = 198061)
F Statistic	505.293*** (df = 40; 198095)	903.938*** (df = 42; 198093)	515.058*** (df = 74; 198061)
Note:	p<0.1; p<0.05; p<0.01

#stargazer(time_modela, time_model1a, time_model2a8, title="Models for Change in Leisure", align=TRUE, type = "html")